In future cognitive radio networks, a number of spectrum sensors can be distributedly deployed to monitor the surrounding wireless environment, where the machine-to-machine (M2M) technology is considered to provide the interactions among sensors, cognitive engines, and other system modules. Thus, a flexible M2M network architecture is desired to develop cognitive radio networks. As a distributed system framework, service-oriented architecture (SOA) has been well studied to provide the loose coupling, reusability, and scalability, where various system function modules are encapsulated into web services to build a virtual system. In this paper, based on SOA, we propose a flexible M2M architecture, namely, the service-oriented radio architecture (SORA), to develop the cognitive radio systems. It shows that our proposed architecture provides coordinated implementation of cognitive radio systems based on different development platforms. Furthermore, an SORA-based cognitive radio testbed is implemented, where the standard web service technologies and open source software tools are used to support the characteristics of SORA.

Intelligence is the most important characteristic for cognitive wireless networks. A cognitive engine built on reconfigurable wireless networks is the key to implementing this characteristic. The design and implementation of a cognitive engine is important in research on the theory of initiative cognition for cognitive wireless networks. This paper first discusses research on cognitive loops, then investigates cognitive functions in the loop through the design of a universal cognitive engine functional architecture, and finally verifies the architecture on the platform of a cognitive engine prototype system. cognitive radio, cognitive wireless network, cognitive loop, cognitive engine, prototype system "Cognition" is the scientific term for the process of thinking. Mitola et al.[1] introduced "cognition" to wireless communication and coined the phrase "Cognitive Radio" in 1999.From then on, cognitive radio and cognitive networks have attracted increased attention from researchers and research institutions [2][3][4], and have become a hotspot in the communication domain [5]. A cognitive network is able to sense information from the outside world and its internal state, and can then adjust the wireless network (including working frequency, air access, data protocols, and so on) by parsing and orienting the sensing information to adapt to changes in the environment. Moreover, a cognitive network can still learn to form new knowledge. Thus, a cognitive network is an intelligent network that can sense, make decisions, and learn like humans. Implementing such a wireless network requires a functional architecture as the brain to control the whole cognitive process, that is, a cognitive engine. The cognitive engine integrates various cognitive functions (such as sensing, learning, and reasoning) so that it can intelligently control a wireless network to implement a cognitive cycle. Rieser [6], a researcher at Virginia Tech, first modeled the biologically inspired cognitive engine called BioCR. Later, his colleague Rondeau [7] further developed the theory of cognitive engines. He evolved cognitive engine modeling as a multi-objective optimization through genetic algorithms and built a cognitive engine architecture composed of a cognitive controller, sensors, a decision maker, optimizers, and interfaces. However, as this cognitive engine was purely designed for waveform optimization, it has limitations. In this paper, we set out to design a universal cognitive engine functional architecture by studying the cognitive loop.

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Based on the Maximum-Likelihood (ML) criterion, this paper proposes a novel noncoherent detection algorithm for Orthogonal Multicode (OM) system in Nakagami fading channel. Some theoretical analysis and simulation results are presented. It is shown that the proposed ML algorithm is at least 0.7 dB better than the conventional Matched-Filter (MF) algorithm for uncoded systems, in both non-fading and fading channels. For the consideration of practical application, it is further simplified in complexity. Compared with the original ML algorithm, the simplified ML algorithm can provide significant reduction in complexity with small degradation in performance. 2 1 log M of the bandwidth of DS-CDMA systems with the same spreading gain. Therefore, the bandwidth efficiency is greatly improved. They are proved to be very suitable for situations that have stringent restriction on communication bandwidth. Later, an orthogonal multicode CDMA system and a biorthogonal multicode CDMA system are proposed to transmit data with even higher rate in Refs.[1] and [4], respectively. As depicted in Fig.1, the input bits with higher rate are first transformed to Q lower rate bit-streams by serial-to-parallel conversion. Afterwards, these Q bit-streams are spread in spectrum by Q sets of orthogonal/biorthogonal codes, respec-1 Manuscript